Project 1

'''
The input image for this challenge can be found in the attachment section. Solve the following problem statements.

1.Script a function to perform image rotation by deriving the rotation matrix for a 140 degree rotation angle.

2.Now on the rotated image perform a scaling operation which reduces the size of the image by 50% of the original size.

3.Take the original image and perform cropping operation manually which focuses on the subject of the input image.

Deliverables:

1.The rotated image

2.The scaled image

3.Cropped image

4.Necessary calculations for the scaling operations which fixes the scaling percentage.

'''

import cv2
import numpy as np

def read_img(img_path):
    
    image = cv2.imread(img_path)
    
    return image

def display_img(image_window,image):
    
    cv2.imshow(image_window,image)
    
    cv2.waitKey(0)

def save_img(img_name,image):
    
    cv2.imwrite(img_name,image)

1.Script a function to perform image rotation by deriving the rotation matrix for a 140 degree rotation angle.

# reading image

image =read_img('Challenge1.png')

# rotation function

def rotation_img(image,angle,scale):

    height,width = image.shape[:2]

    center = (width/2,height/2)

    rotation_matrix = cv2.getRotationMatrix2D(center,145,1.0)

    rotation_img = cv2.warpAffine(image,rotation_matrix,(width,height))
    
    return rotation_img

# calling rotation function and saving image
rotated_img = rotation_img(image,145,1.0)

display_img('rotation_window',rotated_img)

save_img('Saved/rotated_img.png',rotated_img)

2.Now on the rotated image perform a scaling operation which reduces the size of the image by 50% of the original size.

# scaling operation on rotated image


def scale_img(image,scale_percent,method_num):
    
    height = image.shape[0]
    
    width = image.shape[1]
    
    change_height = int((height * scale_percent/100)) 
    
    change_width = int((width * scale_percent/100))
    
    dimensions = (change_width,change_height)
    
    
    method = [cv2.INTER_AREA,cv2.INTER_LINEAR,cv2.INTER_NEAREST,cv2.INTER_CUBIC,cv2.INTER_LANCZOS4]

    scale_method = method[method_num]

    scale_img = cv2.resize(image,dimensions,interpolation = scale_method)
    
    return scale_img


    
        
    

# calling scaling function and saving image

scaled_img = scale_img(rotated_img,50,0)

cv2.imwrite('Saved/scaled_img1.png',scaled_img)

3.Take the original image and perform cropping operation manually which focuses on the subject of the input image.

# cropping function

def crop_img(image,dimensions):
    
    h_s,h_e,w_s,w_e = dimensions
    
    image_dimensions = image.shape
    
    height_dimensions = [h_s,h_e]
    
    width_dimensions = [w_s,w_e]
    
    np.clip(height_dimensions,a_min=0,a_max =image_dimensions[0])
    
    np.clip(width_dimensions,a_min=0,a_max =image_dimensions[1])
    
    crop_img = image[height_dimensions[0]:height_dimensions[1],width_dimensions[0]:width_dimensions[1]]
    
    return crop_img

# cropping image

crop_dimensions = [105,540,180,850]

cropped_image = crop_img(image,crop_dimensions)

display_img('cropped_image_window' , cropped_image)

save_img('Saved/cropped_image.png',cropped_image)